Wireless mobile communication systems have evolved quickly in the recent few decades. After 2G and 3G systems, now the 4th generation (4G) wireless communication system has started to operate. In the 4G system, multiple-input multiple-output (MIMO) transmission technique has played an important role. By utilizing multiple antennas on transmitter and receiver, system reliability and channel capacity can be effectively enhanced. When the transmitter can obtain the chancel state information (CSI) from the feedback of the receiver, MIMO precoding scheme can be conducted. By this way, the channel capacity can be further enhanced. MIMO precoding technique has been proposed in the latest wireless communication standards, such as Worldwide Interoperability for Microwave Access (Wimax), 3GPP Long Term Evolution (LTE), and 3GPP Long Term Evolution Advanced (LTE-A).
Recently, MIMO technique with massive number of antennas (or massive MIMO) has been proposed. With a large number of antennas at the transmitter/receiver, the small-scale channel fading can be easily compensated. Even the simplest match filtering (MF) can do the job. Under this situation, the system can obtain the same performance as that in the AWGN environment even though the channel is actually fading. There are other advantages of massive MIMO. For example, much higher multiuser diversity can be obtained and temporarily shut down of a few RF equipments can be tolerable.
One way to utilize the massive number of antennas is beamforming. By beamforming, the signal to interference plus noise ratio (SINR) at the receiver side can be effectively enhanced. However, the main problem in beamforming is how to determine the beam direction. Conventional beamforming may employ a scanning scheme to obtain direction information, and it usually requires long delay time and high overhead. This problem becomes more apparent when a massive antenna array is deployed in a base station (BS). This is because the BS needs to scan the whole region of the serving area and then determine beam directions from the feedback of user equipments (UEs). It takes a lot of time for scanning and the corresponding feedback overhead from UEs is high.
A location-based beamforming scheme can be applied to overcome the problem. The idea is that if a BS knows a UE location, it knows which direction to beamform. First, the coverage area of a BS is partitioned into regions. The UE then estimate its location and report its region index to the BS. Finally, the BS conducts beamforming based on the reported information. In LTE-A systems, similar localization-based beamforming scheme can also be applied. By utilizing reference signals defined by LTE-A, UEs can employ an observed-time-difference-of-arrival (OTDOA)-based algorithm to estimate its position. The UEs then feedback the position information to their serving BSs. Based on the feedback, the BSs can calculate the beam directions and then conduct beamforming.
The existing OTDOA-based positioning algorithm has certain drawbacks. First, it needs four eNodeBs for 3D positioning. The four eNodeBs provide three range differences that represent three independent hyperbola equations to solve three parameters: the position or coordinates [x, y, z] of UE in 3D. Second, eNodeBs are typically deployed at similar height, e.g., about 25 meters above ground. However, in order to achieve good geometrical dilution of precision (GDOP), the fourth eNodeB needs to be deployed at a relative high position, which introduces additional cost.
A 3D positioning method with reduced cost and satisfactory estimation accuracy is sought.